Assessment of the Performances of the Protein Modeling Techniques Participating in CASP15 Using a Structure-Based Functional Site Prediction Approach: ResiRole.

BACKGROUND: Model quality assessments via computational methods which entail comparisons of the modeled structures to the experimentally determined structures are essential in the field of protein structure prediction. The assessments provide means to benchmark the accuracies of the modeling techniques and to aid with their development. We previously described the ResiRole method to gauge model quality principally based on the preservation of the structural characteristics described in SeqFEATURE functional site prediction models. METHODS: We apply ResiRole to benchmark modeling group performances in the Critical Assessment of Structure Prediction experiment, round 15. To gauge model quality, a normalized Predicted Functional site Similarity Score (PFSS) was calculated as the average of one minus the absolute values of the differences of the functional site prediction probabilities, as found for the experimental structures versus those found at the corresponding sites in the structure models. RESULTS: The average PFSS per modeling group (gPFSS) correlates with standard quality metrics, and can effectively be used to rank the accuracies of the groups. For the free modeling (FM) category, correlation coefficients of the Local Distance Difference Test (LDDT) and Global Distance Test-Total Score (GDT-TS) metrics with gPFSS were 0.98239 and 0.87691, respectively. An example finding for a specific group is that the gPFSS for EMBER3D was higher than expected based on the predictive relationship between gPFSS and LDDT. We infer the result is due to the use of constraints imprinted by function that are a part of the EMBER3D methodology. Also, we find functional site predictions that may guide further functional characterizations of the respective proteins. CONCLUSION: The gPFSS metric provides an effective means to assess and rank the performances of the structure prediction techniques according to their abilities to accurately recount the structural features at predicted functional sites.

The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases.

We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.

ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques.

MOTIVATION: Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. RESULTS: Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. AVAILABILITYAND IMPLEMENTATION: Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Quantitative and Narrative Evaluation of Goodman and Gilman's Pharmacological Basis of Therapeutics.

Goodman and Gilman's The Pharmacological Basis of Therapeutics (GGPBT) has been a cornerstone in the education of pharmacists, physicians, and pharmacologists for decades. The objectives of this study were to describe and evaluate the 13th edition of GGPBT on bases including: (1) author characteristics; (2) recency of citations; (3) conflict of interest (CoI) disclosure; (4) expert evaluation of chapters. Contributors' (N = 115) sex, professional degrees, and presence of undisclosed potential CoI-as reported by the Center for Medicare and Medicaid's Open Payments (2013-2017)-were examined. The year of publication of citations was extracted relative to Katzung's Basic and Clinical Pharmacology (KatBCP), and DiPiro's Pharmacotherapy: A Pathophysiologic Approach (DiPPAPA). Content experts provided thorough chapter reviews. The percent of GGPBT contributors that were female (20.9%) was equivalent to those in KatBCP (17.0%). Citations in GGPBT (11.5 ± 0.2 years) were significantly older than those in KatBCP (10.4 ± 0.2) and DiPPAPA (9.1 ± 0.1, p < 0.0001). Contributors to GGPBT received USD 3 million in undisclosed remuneration (Maximum author = USD 743,718). In contrast, DiPPAPA made CoI information available. Reviewers noted several strengths but also some areas for improvement. GGPBT will continue to be an important component of the biomedical curriculum. Areas of improvement include a more diverse authorship, improved conflict of interest transparency, and a greater inclusion of more recent citations.

Site-specific phosphorylation regulates the structure and function of an intrinsically disordered domain of the glucocorticoid receptor.

Intrinsically disordered (ID) regions of the transcription factor proteins have much larger frequency of phosphorylation sites than ordered regions, suggesting an important role in their regulatory capacity. Consistent with this phenomenon, most of the functionally known phosphorylation sites in the steroid receptor family of transcription factors are located in the ID N-terminal domain that contains a powerful activation function (AF1) region. In this study, we determined the structural and functional consequences of functionally known phosphorylation residues (Ser203, 211, and 226) located in the human glucocorticoid receptor's (GR's) ID AF1 domain. We report the relative importance of each phosphorylation site in inducing a functionally active ordered conformation in GR's ID AF1 domain. Our data demonstrate a mechanism through which ID domain of the steroid receptors and other similar transcription factors may adopt a functionally active conformation under physiological conditions.

Implementation of the Connective Tissue Screening Questionnaire in northeast Pennsylvania to identify comorbidities of connective tissue diseases in subjects with systemic lupus erythematosus.

Previous studies have described an increased risk of developing an additional connective tissue disease (CTD) when one such ailment is present. We examine here the likelihood that individuals with systemic lupus erythematosus (SLE) screen positive for one or more of the following five autoimmune CTDs: Sjögren's syndrome, scleroderma, rheumatoid arthritis, dermatomyositis/polymyositis, and mixed connective tissue disorder. Five hundred SLE-diagnosed subjects were asked to complete a CTD screening questionnaire (CSQ). The results were analyzed according to the set of diagnostic criteria given by the American College of Rheumatology to identify probable cases of each CTD. Significant standardized prevalence ratios and comorbidities indicate an increased risk for the other autoimmune CTDs. In all, 96% of the subjects screened positive for at least one additional CTD, and 13% screened positive for at least two additional CTDs. We see that the SLE-diagnosed population may benefit from further attention regarding the presence of additional CTDs, which may further inform treatment strategies. We also see the application of the CSQ as a potentially important tool for clinical practice, and we describe the present study's limitations along with possible ways that these can be addressed.

Quantification of the impact of PSI:Biology according to the annotations of the determined structures.

BACKGROUND: Protein Structure Initiative:Biology (PSI:Biology) is the third phase of PSI where protein structures are determined in high-throughput to characterize their biological functions. The transition to the third phase entailed the formation of PSI:Biology Partnerships which are composed of structural genomics centers and biomedical science laboratories. We present a method to examine the impact of protein structures determined under the auspices of PSI:Biology by measuring their rates of annotations. The mean numbers of annotations per structure and per residue are examined. These are designed to provide measures of the amount of structure to function connections that can be leveraged from each structure. RESULTS: One result is that PSI:Biology structures are found to have a higher rate of annotations than structures determined during the first two phases of PSI. A second result is that the subset of PSI:Biology structures determined through PSI:Biology Partnerships have a higher rate of annotations than those determined exclusive of those partnerships. Both results hold when the annotation rates are examined either at the level of the entire protein or for annotations that are known to fall at specific residues within the portion of the protein that has a determined structure. CONCLUSIONS: We conclude that PSI:Biology determines structures that are estimated to have a higher degree of biomedical interest than those determined during the first two phases of PSI based on a broad array of biomedical annotations. For the PSI:Biology Partnerships, we see that there is an associated added value that represents part of the progress toward the goals of PSI:Biology. We interpret the added value to mean that team-based structural biology projects that utilize the expertise and technologies of structural genomics centers together with biological laboratories in the community are conducted in a synergistic manner. We show that the annotation rates can be used in conjunction with established metrics, i.e. the numbers of structures and impact of publication records, to monitor the progress of PSI:Biology towards its goals of examining structure to function connections of high biomedical relevance. The metric provides an objective means to quantify the overall impact of PSI:Biology as it uses biomedical annotations from external sources.

KB-Rank: efficient protein structure and functional annotation identification via text query.

The KB-Rank tool was developed to help determine the functions of proteins. A user provides text query and protein structures are retrieved together with their functional annotation categories. Structures and annotation categories are ranked according to their estimated relevance to the queried text. The algorithm for ranking first retrieves matches between the query text and the text fields associated with the structures. The structures are next ordered by their relative content of annotations that are found to be prevalent across all the structures retrieved. An interactive web interface was implemented to navigate and interpret the relevance of the structures and annotation categories retrieved by a given search. The aim of the KB-Rank tool is to provide a means to quickly identify protein structures of interest and the annotations most relevant to the queries posed by a user. Informational and navigational searches regarding disease topics are described to illustrate the tool's utilities. The tool is available at the URL http://protein.tcmedc.org/KB-Rank.

The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods.

The Protein Structure Initiative's Structural Biology Knowledgebase (SBKB, URL: http://sbkb.org ) is an open web resource designed to turn the products of the structural genomics and structural biology efforts into knowledge that can be used by the biological community to understand living systems and disease. Here we will present examples on how to use the SBKB to enable biological research. For example, a protein sequence or Protein Data Bank (PDB) structure ID search will provide a list of related protein structures in the PDB, associated biological descriptions (annotations), homology models, structural genomics protein target status, experimental protocols, and the ability to order available DNA clones from the PSI:Biology-Materials Repository. A text search will find publication and technology reports resulting from the PSI's high-throughput research efforts. Web tools that aid in research, including a system that accepts protein structure requests from the community, will also be described. Created in collaboration with the Nature Publishing Group, the Structural Biology Knowledgebase monthly update also provides a research library, editorials about new research advances, news, and an events calendar to present a broader view of structural genomics and structural biology.

A new approach to assess and predict the functional roles of proteins across all known structures.

The three dimensional atomic structures of proteins provide information regarding their function; and codified relationships between structure and function enable the assessment of function from structure. In the current study, a new data mining tool was implemented that checks current gene ontology (GO) annotations and predicts new ones across all the protein structures available in the Protein Data Bank (PDB). The tool overcomes some of the challenges of utilizing large amounts of protein annotation and measurement information to form correspondences between protein structure and function. Protein attributes were extracted from the Structural Biology Knowledgebase and open source biological databases. Based on the presence or absence of a given set of attributes, a given protein's functional annotations were inferred. The results show that attributes derived from the three dimensional structures of proteins enhanced predictions over that using attributes only derived from primary amino acid sequence. Some predictions reflected known but not completely documented GO annotations. For example, predictions for the GO term for copper ion binding reflected used information a copper ion was known to interact with the protein based on information in a ligand interaction database. Other predictions were novel and require further experimental validation. These include predictions for proteins labeled as unknown function in the PDB. Two examples are a role in the regulation of transcription for the protein AF1396 from Archaeoglobus fulgidus and a role in RNA metabolism for the protein psuG from Thermotoga maritima.

The identification of novel cyclic AMP-dependent protein kinase anchoring proteins using bioinformatic filters and peptide arrays.

A-kinase anchoring proteins (AKAPs) localize cyclic AMP-dependent protein kinase (PKA) to specific regions in the cell and place PKA in proximity to its phosphorylation targets. A computational model was created to identify AKAPs that bind to the docking/dimerization domain of the RII alpha isoform of the regulatory subunit of PKA. The model was used to search the entire human proteome, and the top candidates were tested for an interaction using peptide array experiments. Verified interactions include sphingosine kinase interacting protein and retinoic acid-induced protein 16. These interactions highlight new signaling pathways mediated by PKA.

Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains.

Extensive efforts have been devoted to determining the binding specificity of Src homology 3 (SH3) domains usually in a case-by-case manner. A generic structure-based model is necessary to decipher the protein recognition code of the entire domain family. In this study, we have developed a general framework that combines molecular modeling and a machine learning algorithm to capture the energetic characteristics of the domain-peptide interactions and predict the binding specificity of the SH3 domain family. Our model is not trained for individual SH3 domains; rather it is a generic model for the entire domain family. Our model not only achieved satisfactory prediction accuracy but also provided structural insights into which residues are important for the binding specificity. The success of our framework on SH3 domains suggests that it is possible to establish a theoretical model to decipher the protein recognition code of any modular domain.

Entropic contributions and the influence of the hydrophobic environment in promiscuous protein-protein association.

The mechanisms by which a promiscuous protein can strongly interact with several different proteins using the same binding interface are not completely understood. An example is protein kinase A (PKA), which uses a single face on its docking/dimerization domain to interact with multiple A-kinase anchoring proteins (AKAP) that localize it to different parts of the cell. In the current study, the configurational entropy contributions to the binding between the AKAP protein HT31 with the D/D domain of RII alpha-regulatory subunit of PKA were examined. The results show that the majority of configurational entropy loss for the interaction was due to decreased fluctuations within rotamer states of the side chains. The result is in contrast to the widely held approximation that the decrease in the number of rotamer states available to the side chains forms the major component. Further analysis showed that there was a direct linear relationship between total configurational entropy and the number of favorable, alternative contacts available within hydrophobic environments. The hydrophobic binding pocket of the D/D domain provides alternative contact points for the side chains of AKAP peptides that allow them to adopt different binding conformations. The increase in binding conformations provides an increase in binding entropy and hence binding affinity. We infer that a general strategy for a promiscuous protein is to provide alternative contact points at its interface to increase binding affinity while the plasticity required for binding to multiple partners is retained. Implications are discussed for understanding and treating diseases in which promiscuous protein interactions are used.

Evaluating the potency of HIV-1 protease drugs to combat resistance.

HIV-1 protease has been an important drug target for the antiretroviral treatment of HIV infection. The efficacy of protease drugs is impaired by the rapid emergence of resistant virus strains. Understanding the molecular basis and evaluating the potency of an inhibitor to combat resistance are no doubt important in AIDS therapy. In this study, we first identified residues that have significant contributions to binding with six substrates using molecular dynamics simulations and Molecular Mechanics Generalized Born Surface Area calculations. Among the critical residues, Asp25, Gly27, Ala28, Asp29, and Gly49 are well conserved, with which the potent drugs should form strong interactions. We then calculated the contribution of each residue to binding with eight FDA approved drugs. We analyzed the conservation of each protease residue and also compared the interaction between the HIV protease and individual residues of the drugs and substrates. Our analyses showed that resistant mutations usually occur at less conserved residues forming more favorable interactions with drugs than with substrates. To quantitatively integrate the binding free energy and conservation information, we defined an empirical parameter called free energy/variability (FV) value, which is the product of the contribution of a single residue to the binding free energy and the sequence variability at that position. As a validation, the FV value was shown to identify single resistant mutations with an accuracy of 88%. Finally, we evaluated the potency of a newly approved drug, darunavir, to combat resistance and predicted that darunavir is more potent than amprenavir but may be susceptible to mutations on Val32 and Ile84.

On the detection of functionally coherent groups of protein domains with an extension to protein annotation.

BACKGROUND: Protein domains coordinate to perform multifaceted cellular functions, and domain combinations serve as the functional building blocks of the cell. The available methods to identify functional domain combinations are limited in their scope, e.g. to the identification of combinations falling within individual proteins or within specific regions in a translated genome. Further effort is needed to identify groups of domains that span across two or more proteins and are linked by a cooperative function. Such functional domain combinations can be useful for protein annotation. RESULTS: Using a new computational method, we have identified 114 groups of domains, referred to as domain assembly units (DASSEM units), in the proteome of budding yeast Saccharomyces cerevisiae. The units participate in many important cellular processes such as transcription regulation, translation initiation, and mRNA splicing. Within the units the domains were found to function in a cooperative manner; and each domain contributed to a different aspect of the unit's overall function. The member domains of DASSEM units were found to be significantly enriched among proteins contained in transcription modules, defined as genes sharing similar expression profiles and presumably similar functions. The observation further confirmed the functional coherence of DASSEM units. The functional linkages of units were found in both functionally characterized and uncharacterized proteins, which enabled the assessment of protein function based on domain composition. CONCLUSION: A new computational method was developed to identify groups of domains that are linked by a common function in the proteome of Saccharomyces cerevisiae. These groups can either lie within individual proteins or span across different proteins. We propose that the functional linkages among the domains within the DASSEM units can be used as a non-homology based tool to annotate uncharacterized proteins.

Prediction of binding sites of peptide recognition domains: an application on Grb2 and SAP SH2 domains.

Determination of the binding motif and identification of interaction partners of the modular domains such as SH2 domains can enhance our understanding of the regulatory mechanism of protein-protein interactions. We propose here a new computational method to achieve this goal by integrating the orthogonal information obtained from binding free energy estimation and peptide sequence analysis. We performed a proof-of-concept study on the SH2 domains of SAP and Grb2 proteins. The method involves the following steps: (1) estimating the binding free energy of a set of randomly selected peptides along with a sample of known binders; (2) clustering all these peptides using sequence and energy characteristics; (3) extracting a sequence motif, which is represented by a hidden Markov model (HMM), from the cluster of peptides containing the sample of known binders; and (4) scanning the human proteome to identify binding sites of the domain. The binding motifs of the SAP and Grb2 SH2 domains derived by the method agree well with those determined through experimental studies. Using the derived binding motifs, we have predicted new possible interaction partners for the Grb2 and SAP SH2 domains as well as possible interaction sites for interaction partners already known. We also suggested novel roles for the proteins by reviewing their top interaction candidates.

Computational analysis and prediction of the binding motif and protein interacting partners of the Abl SH3 domain.

Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains, such as Src Homology 2 and 3 (SH2 and SH3) domains, which bind to specific sequence and structural motifs. It is important but challenging to determine the binding specificity of these domains accurately and to predict their physiological interacting partners. In this study, the interactions between 35 peptide ligands (15 binders and 20 non-binders) and the Abl SH3 domain were analyzed using molecular dynamics simulation and the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. The calculated binding free energies correlated well with the rank order of the binding peptides and clearly distinguished binders from non-binders. Free energy component analysis revealed that the van der Waals interactions dictate the binding strength of peptides, whereas the binding specificity is determined by the electrostatic interaction and the polar contribution of desolvation. The binding motif of the Abl SH3 domain was then determined by a virtual mutagenesis method, which mutates the residue at each position of the template peptide relative to all other 19 amino acids and calculates the binding free energy difference between the template and the mutated peptides using the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. A single position mutation free energy profile was thus established and used as a scoring matrix to search peptides recognized by the Abl SH3 domain in the human genome. Our approach successfully picked ten out of 13 experimentally determined binding partners of the Abl SH3 domain among the top 600 candidates from the 218,540 decapeptides with the PXXP motif in the SWISS-PROT database. We expect that this physical-principle based method can be applied to other protein domains as well.

A structure-based method for identifying DNA-binding proteins and their sites of DNA-interaction.

A classification model of a DNA-binding protein chain was created based on identification of alpha helices within the chain likely to bind to DNA. Using the model, all chains in the Protein Data Bank were classified. For many of the chains classified with high confidence, previous documentation for DNA-binding was found, yet no sequence homology to the structures used to train the model was detected. The result indicates that the chain model can be used to supplement sequence based methods for annotating the function of DNA-binding. Four new candidates for DNA-binding were found, including two structures solved through structural genomics efforts. For each of the candidate structures, possible sites of DNA-binding are indicated by listing the residue ranges of alpha helices likely to interact with DNA.

Statistical models for discerning protein structures containing the DNA-binding helix-turn-helix motif.

A method for discerning protein structures containing the DNA-binding helix-turn-helix (HTH) motif has been developed. The method uses statistical models based on geometrical measurements of the motif. With a decision tree model, key structural features required for DNA binding were identified. These include a high average solvent-accessibility of residues within the recognition helix and a conserved hydrophobic interaction between the recognition helix and the second alpha helix preceding it. The Protein Data Bank was searched using a more accurate model of the motif created using the Adaboost algorithm to identify structures that have a high probability of containing the motif, including those that had not been reported previously.